Computer games and multimedia applications have begun employing cameras and software gesture recognition engines to provide a human computer interface (“HCI”). With HCI, user body parts and movements are detected, interpreted and used to control game characters or other aspects of an application. One difficulty in HCI systems is identifying body parts. Exemplar classification is one method in which every pixel is assigned a probability distribution over body parts. Taking one body part at a time, a new, monochrome image is derived in which a pixel is assigned a probability as the likelihood it belongs to the given part. There are many pixels, however, and it benefits performance if the classification results are thinned to a set of prototypical locations, which are referred to herein as centroids.
For performance, it would be ideal if a single centroid were produced for each body part, but this is unlikely. Exemplar, which classifies a pixel based on local features, can yield multiple, disjoint regions of high probability. At most, only one of these will correspond to the true body part. Calculating a global centroid will average pixels relating to the proper body part with those from misclassified regions. This is unreliable as a summary of the exemplar output because it will fall at the center of these multiple regions with no guarantee that it will itself be a meaningful point.
A method is therefore needed to compute zero or more meaningful centroids for a body part, where each centroid is coincident with a region of non-zero probability in the exemplar output.